Name
Abstract | ||
|---|---|---|
Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in high-dimensional space, a limited number of potential outliers may fail to provide sufficient information to assist the classifier in describing a boundary that can separate outliers from normal data effectively. To address this, we propose a novel Single-Objective Generative Adversarial Active Learning (SO-GAAL) method for outlier detection, which can directly generate informative potential outliers based on the mini-max game between a generator and a discriminator. Moreover, to prevent the generator from falling into the mode collapsing problem, the stop node of training should be determined when SO-GAAL is able to provide sufficient information. But without any prior information, it is extremely difficult for SO-GAAL. Therefore, we expand the network structure of SO-GAAL from a single generator to multiple generators with different objectives (MO-GAAL), which can generate a reasonable reference distribution for the whole dataset. We empirically compare the proposed approach with several state-of-the-art outlier detection methods on both synthetic and real-world datasets. The results show that MO-GAAL outperforms its competitors in the majority of cases, especially for datasets with various cluster types or high irrelevant variable ratio. The experiment codes are available at:
<uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/leibinghe/GAAL-based-outlier-detection</uri>
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TKDE.2019.2905606 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Anomaly detection,Generators,Computational modeling,Data models,Training,Generative adversarial networks,Gallium nitride | Anomaly detection,Active learning,Discriminator,Mode (statistics),Outlier,Artificial intelligence,Sampling (statistics),Generative grammar,Classifier (linguistics),Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
32 | 8 | 1041-4347 |
Citations | PageRank | References |
13 | 0.56 | 33 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yezheng Liu | 1 | 145 | 24.69 |
| Zhe Li | 2 | 13 | 0.56 |
| Chong Zhou | 3 | 93 | 3.20 |
| Yuanchun Jiang | 4 | 184 | 21.24 |
| Jianshan Sun | 5 | 192 | 17.65 |
| Mingyu Wang | 6 | 13 | 0.56 |
| Xiangnan He | 7 | 3064 | 128.86 |